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Biomedical subjects

William Wells

Publications and source records attributed to William Wells.

7 recordsLinked to original sources
PubMed2006

Arresting developments: trends in female arrests for domestic violence and proposed explanations.

This article represents an effort to generate more systematic and specified discussion on the topic of unintended consequences in the movement to decrease violence against women. In this case, the consequence is increases in female arrests for domestic violence. This article builds on recent discussions by first using a sample of data to examine felony domestic violence arrest rates for men and women. The data support the conclusion that domestic violence arrests of women have increased. Second, the article presents six explanations that are derived from existing literature. Although the authors do not offer empirical tests of these explanations, this presentation can play an important part in better understanding the outcomes of criminal justice policies that are aimed at increasing victim safety.

California↗
PubMed2005

No more 'silent stooges'. Interview by Alexis Nolan.

Non-executive directors are becoming increasingly difficult to recruit, according to Sir William Wells, chair of the NHS Appointments Commission. He also warns that foundation trust governors face a stiff test in their ability to appoint non-execs and avoid 'favoured sons or daughters'. In an interview with SHJ, he also calls for a greater role for the commission in the development of non-execs.

Foundations↗
PubMed2004

A new visualization technique for laparoscopic ultrasonography.

BACKGROUND: Using laparoscopic ultrasonography (LUS) is challenging for both novice and experienced ultrasonographers. The major difficulty surgeons experience is understanding the orientation of the ultrasonography image. The purpose of this study was to assess whether providing surgeons with orientation information improves their ability to interpret LUS images. METHODS: We performed a LUS examination on a 25-kg pig and simultaneously digitized video from the laparoscopic camera, the LUS, and a novel orientation system. From the video recordings, 12 different clips of intra-abdominal anatomy were prepared. Twenty surgeons (18 staff, 2 fellows) volunteered to participate in an experimental crossover study. Test subjects reviewed the LUS clips along with the laparoscopic video images and the orientation display. Controls reviewed the LUS clips with only the laparoscopic video images. Diagnostic accuracy was compared by using the odds ratio. RESULTS: For all vessels, the orientation display improved the odds ratio for correctly identifying structures from 3.7 to 8.9 (P=.02). For arteries, the orientation display improved the odds ratio from 2.4 to 9.6 (P=.01). For veins, the orientation display improved the odds ratio from 4.4 to 13.6 (P=.04). CONCLUSIONS: Providing orientation information significantly improves a surgeon's ability to interpret LUS images.

Animals↗
PubMed2003

A shape-based approach to the segmentation of medical imagery using level sets.

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras, we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.

Algorithms↗
PubMed2003

Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier.

A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor "ground truth." Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(+/-0.064), and our best SVM classifier achieved an average ROC area of 0.761(+/-0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(+/-0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance.

Algorithms↗